The long-term objective of this research is to enable coordinated, optimal expression of multiple genes for metabolic pathways in eukaryotes. The ultimate goal is to enable metabolic engineering for maximizing pathway flux. This would have application in the correction of metabolic imbalances or for the efficient formation of metabolites in microbes, plants and animals. The technology could eventually be used to engineer complex traits. Large-scale expression is the next step in metabolic engineering of eukaryotic cells. The specific objective of this work is to maximize the flux of metabolites from xylose to ethanol by manipulating the expression of 15 to 20 genes in the central pentose phosphate, glycolytic and TCA cycle pathways of Saccharomyces cerevisiae. The research will develop a technology for the systematic, large-scale expression of genes from synthetic artificial chromosomes (SACs) created from defined genetic elements. This technology will enable the engineering of large, complex metabolic pathways for the balanced synthesis of selected products. It will be highly applicable to various eukaryotic expression systems. Multiple genes will be introduced into DNA segments by extension overlap PCR, ligation and concatenation. A series of individual genes and defined genomic elements will be synthesized using appropriate promoters and linkers, ligated together into modules and expressed in S. cerevisiae. This work will test the hypothesis that over expression of genes for one part of a pathway simply results in the accumulation of intermediates in another part. If one gene is over expressed, another becomes limiting. Metabolic efficiency is impaired by imbalanced enzyme synthesis, and over expression of some genes can inhibit cell growth. This research will approach an optimum for gene expression in a complete pathway. By examining transformants for growth on and fermentation of xylose, which the parental cell cannot assimilate, it will determine effects of SACs on biochemistry metabolite flux, and cellular physiology. It will measure expression levels of the introduced genes using real-time PCR. The effects of SACs on the accumulation of transcripts for other genes will be evaluated in selected transformants by genome-wide expression studies. Finally, the effects of SACs on metabolite levels will be assessed by a combination of 31P NMR and LC/MS. It will employ statistical methods to interpret these results and approach an optimum level of metabolite flux through iterative trials.